Performance evaluation of deep learning-based osteoporosis diagnostic models with conventional chest X-ray in a clinical cohort.

BACKGROUND

Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis; however, its limited accessibility often hinders routine screening in primary care settings. To address this gap, we developed and evaluated a deep learning-based model, PROS® CXR: OSTEO (Promedius, Inc., Seoul, South Korea), which predicts osteoporosis from conventional chest radiographs.

METHODS

This retrospective study included 80 adult patients who underwent both DXA and chest radiography within a 3-month interval. The deep learning model, based on convolutional neural networks and trained via transfer learning, generated osteoporosis predictions from chest X-rays. Model performance was assessed against DXA-derived T-scores of the femur and lumbar spine, using either the minimum or average T-score per site as the reference standard.

RESULTS

The proposed model achieved an area under the curve (AUC) of 0.94 for femur and 0.93 for lumbar spine predictions. For osteoporosis screening, the sensitivity and specificity were 90% and 81%, respectively. Subgroup analysis demonstrated higher predictive performance in female patients, whereas false-positives (FPs) occurred more frequently in males.

CONCLUSIONS

The PROS® CXR: OSTEO model enables opportunistic and low-cost osteoporosis screening using routine chest radiographs. This approach holds promise for early detection in aging populations and resource-limited settings. Further optimization is required to improve specificity and minimize FPs before clinical implementation.